# Machine Learning-Driven Muscle Fatigue Estimation in Resistance Training with Assistive Robotics

## TL;DR

This paper introduces a machine learning method to estimate muscle fatigue during resistance training using force-time data, offering a more accurate and automated alternative to traditional methods.

## Contribution

The novel use of engineered features in a machine learning model to predict perceived exertion from force-time data in resistance training.

## Key findings

- RPE is more closely related to relative fatigue progression than absolute biomechanical output.
- Random Forest model achieved over 93% accuracy in predicting RPE within ±1 unit.
- Engineered features significantly improved predictive performance.

## Abstract

Monitoring muscle fatigue is essential for ensuring safety and maximizing the effectiveness of resistance training. Conventional methods such as electromyography (EMG), inertial measurement units (IMU), and ratings of perceived exertion (RPE) involve complex procedures and have limited applicability, particularly in unsupervised or robotic exercise environments. This study proposes a machine learning-based approach to directly predict RPE from force–time data collected during repeated isokinetic bench press sets. Thirty-two male participants (64 limb datasets) performed seven sets at a standardized 7RM load, with load cell data and RPE scores recorded. Biomechanical features representing magnitude, variability, energy, and temporal dynamics were extracted, along with engineered features reflecting relative changes and inter-set variations. The findings indicate that RPE is more closely related to relative fatigue progression than to absolute biomechanical output. Incorporating engineered features substantially improved predictive performance, with the Random Forest model achieving the highest accuracy and more than 93% of predictions falling within ±1 RPE unit of the reported values. The proposed approach can be seamlessly integrated into intelligent resistance machines, enabling automated load adjustment and providing substantial potential for applications in both athletic training and rehabilitation contexts.